Key Highlights: What This Case Study Covers
- Best practices in data warehouse modernization and lakehouse architecture for life sciences, including cross-system batch traceability and genealogy modeling.
- Deployment of a Medallion Delta Lake architecture and automated ETL pipelines to enable real-time batch monitoring and continuous process validation in compliance-driven environments.
- How to build a scalable, automated biopharma analytics platform that unifies multi-site operational telemetry across global manufacturing facilities with governed data pipelines.
Client Overview
Our client is a global biopharmaceutical leader. Known for producing life-saving plasma-derived therapies, they specialize in Immunoglobulin (Ig) and Albumin product portfolios across global markets.
The Ask
The client planned to modernize its Data platform by transitioning from Amazon Redshift and legacy systems to Databricks cloud platform. The goal was to consolidate data silos, enable end-to-end batch traceability, automate data pipelines, and establish a governed, auditable single source of truth for manufacturing and quality analytics.
Challenges
- Integrating siloed operational datasets from enterprise platforms, including SAP HANA, MES, LIMS, and QMS, into a unified data ecosystem.
- Streamlining end-to-end batch genealogy and product mapping to transition from manual tracking to automated, high-visibility reporting.
- Optimizing data ingestion and processing workflows to move from fragmented manual tasks to efficient, consistent, and automated pipelines.
- Establishing comprehensive end-to-end data lineage and auditable pipelines to strengthen compliance frameworks and streamline regulatory processes.
Our Solution: Manufacturing & Quality Data Platform Modernization
- Migrated Legacy Warehouse to Cloud Infrastructure: Transitioned legacy SQL data warehouse components to Amazon Redshift to support high-performance SQL servicing and enterprise data queries.
- Architected Medallion Lakehouse Structure: Structured raw and processed data into Bronze (Raw), Silver (Standardized), and Gold (Business Model) layers using Azure Databricks to clean, harmonize, and enrich telemetry.
- Automated Multi-Source Data Ingestion: Implemented automated extraction pipelines using Apache NiFi, Fivetran, and Azure Databricks Spark to ingest data from SAP HANA, Salesforce Cloud, SharePoint Lists, Rapid Response, and Star LIMS.
- Engineered Standardized Batch Data Products: Developed PySpark and Spark SQL pipelines to create unified data entities, including Batch, Batch Genealogy, and Batch-to-Product models for cross-system harmonization.
- Orchestrated Workflows with Automated DevOps: Managed pipeline workflows with Databricks Native Scheduler and enforced continuous integration/deployment using Bitbucket.
- Enabled Advanced BI & ML Sandbox: Enabled self-service dashboards through Power BI Premium Datasets and established an MLflow sandbox environment to accelerate predictive analytics and machine learning use cases.
Impact Delivered
- Provided real-time visibility into manufacturing, quality testing, and distribution processes across operating sites.
- Drastically reduced reporting cycles by automating manual ingestion pipelines and standardizing enterprise data models.
- Enabled continuous process validation, early detection of process deviations, and real-time quality trend monitoring.
- Established fully auditable data pipelines, transparent data lineage, and governed access controls to satisfy biopharma compliance mandates.
- Standardized data quality and validation checks and established trusted, reliable datasets for the Manufacturing, Quality, and Operations teams.
